.
Repeated Measures and Longitudinal Data
Study Course Description
Course Description Statuss:Approved
Course Description Version:5.00
Study Course Accepted:14.03.2024 11:50:07
| Study Course Information | |||||||||
| Course Code: | SL_113 | LQF level: | Level 7 | ||||||
| Credit Points: | 2.00 | ECTS: | 3.00 | ||||||
| Branch of Science: | Mathematics; Theory of Probability and Mathematical Statistics | Target Audience: | Life Science | ||||||
| Study Course Supervisor | |||||||||
| Course Supervisor: | Ziad Taib | ||||||||
| Study Course Implementer | |||||||||
| Structural Unit: | Statistics Unit | ||||||||
| The Head of Structural Unit: | |||||||||
| Contacts: | 14 Baložu street, 2nd floor, Riga, statistika rsu[pnkts]lv, +371 67060897 | ||||||||
| Study Course Planning | |||||||||
| Full-Time - Semester No.1 | |||||||||
| Lectures (count) | 6 | Lecture Length (academic hours) | 2 | Total Contact Hours of Lectures | 12 | ||||
| Classes (count) | 4 | Class Length (academic hours) | 3 | Total Contact Hours of Classes | 12 | ||||
| Total Contact Hours | 24 | ||||||||
| Part-Time - Semester No.1 | |||||||||
| Lectures (count) | 6 | Lecture Length (academic hours) | 1 | Total Contact Hours of Lectures | 6 | ||||
| Classes (count) | 4 | Class Length (academic hours) | 2 | Total Contact Hours of Classes | 8 | ||||
| Total Contact Hours | 14 | ||||||||
| Study course description | |||||||||
| Preliminary Knowledge: | To follow this course, the student is required to be familiar with some basic mathematical and statistical concepts. Moreover, some computer skills are also required. | ||||||||
| Objective: | This course provides knowledge in the field of repeated measures which has become a necessary tool for analysing data involving e.g. random effects, correlated observations and missing data. The emphasis is on continuous longitudinal data and on how to use SAS and R to model and analyse repeated models. However, other types of repeated measures such as hierarchical models will also be discussed. The purpose of this course is to provide idea and tools for mixed model methods. Such methods can be applied to a variety of situations involving correlated data such as in longitudinal data, clustered data, repeated measures and hierarchical analysis. Generalized models will also be touched upon briefly. The course aims to enable the participants to formulate a mixed model, define and interpret possible estimators, and implement a mixed model analysis for e.g. a repeated measures study. | ||||||||
| Topic Layout (Full-Time) | |||||||||
| No. | Topic | Type of Implementation | Number | Venue | |||||
| 1 | Definitions and introduction to repeated measures data and to normal mixed models. Model fitting, estimation and hypothesis testing. | Lectures | 1.00 | auditorium | |||||
| 2 | Normal mixed models: The Bayesian approach the random effect. Software for fitting mixed models: packages for fitting mixed models. | Lectures | 1.00 | auditorium | |||||
| 3 | Computer lab 1: Introduction to SAS and R for mixed models and estimation and testing in SAS and R. | Classes | 1.00 | computer room | |||||
| 4 | Generalised linear mixed models for categorical data. | Lectures | 1.00 | auditorium | |||||
| 5 | Computer lab 2: mixed logistic regression. | Classes | 1.00 | computer room | |||||
| 6 | Covariance patterns for mixed models and sample size estimation. | Lectures | 1.00 | auditorium | |||||
| 7 | Missing data and multiple imputation. Residuals and goodness of fit in mixed models. | Lectures | 1.00 | auditorium | |||||
| 8 | Computer lab 3: Sample Size Estimation, Missing data and multiple imputation. | Classes | 1.00 | computer room | |||||
| 9 | Random coefficients models and repetition / preparation for the exam. | Lectures | 1.00 | auditorium | |||||
| 10 | Computer lab 4: Random coefficients models. | Classes | 1.00 | computer room | |||||
| Topic Layout (Part-Time) | |||||||||
| No. | Topic | Type of Implementation | Number | Venue | |||||
| 1 | Definitions and introduction to repeated measures data and to normal mixed models. Model fitting, estimation and hypothesis testing. | Lectures | 1.00 | auditorium | |||||
| 2 | Normal mixed models: The Bayesian approach the random effect. Software for fitting mixed models: packages for fitting mixed models. | Lectures | 1.00 | auditorium | |||||
| 3 | Computer lab 1: Introduction to SAS and R for mixed models and estimation and testing in SAS and R. | Classes | 1.00 | computer room | |||||
| 4 | Generalised linear mixed models for categorical data. | Lectures | 1.00 | auditorium | |||||
| 5 | Computer lab 2: mixed logistic regression. | Classes | 1.00 | computer room | |||||
| 6 | Covariance patterns for mixed models and sample size estimation. | Lectures | 1.00 | auditorium | |||||
| 7 | Missing data and multiple imputation. Residuals and goodness of fit in mixed models. | Lectures | 1.00 | auditorium | |||||
| 8 | Computer lab 3: Sample Size Estimation, Missing data and multiple imputation. | Classes | 1.00 | computer room | |||||
| 9 | Random coefficients models and repetition / preparation for the exam. | Lectures | 1.00 | auditorium | |||||
| 10 | Computer lab 4: Random coefficients models. | Classes | 1.00 | computer room | |||||
| Assessment | |||||||||
| Unaided Work: | • Individual work with the course material and compulsory literature in preparation to 6 lectures according to plan. • 4 computer projects – individual work in pairs on agreed computer assignments. Students will analyse data to reach requirements of defined tasks with mixed models presented throughout the course. In order to evaluate the quality of the study course as a whole, the student must fill out the study course evaluation questionnaire on the Student Portal. | ||||||||
| Assessment Criteria: | Assessment on the 10-point scale according to the RSU Educational Order: • Active participation in lectures, exercises and computer projects – 20%. • Final written examination – 40%. • Handing out reports on compulsory 4 computer projects – 40%. | ||||||||
| Final Examination (Full-Time): | Exam (Written) | ||||||||
| Final Examination (Part-Time): | Exam (Written) | ||||||||
| Learning Outcomes | |||||||||
| Knowledge: | After the course acquisition students will know in-depth mixed models with emphasis on biomedical applications to process repeated measures and longitudinal data. This includes using SAS and R through practical sessions to analyse real life data. | ||||||||
| Skills: | The students will be able to: • write and interpret mixed models for longitudinal data of different study designs. • critically evaluate and interpret statistical inference for mixed models and longitudinal data. • choose, apply, and interact with statistical software for mixed models. | ||||||||
| Competencies: | After passing the course, the student will be competent to use the mixed model framework, to describe and analyse qualitatively common study designs and models with longitudinal data or otherwise correlated observations, conduct an appropriate statistical analysis of models covered in the course using software, the latest scientific knowledge, creative and innovative solutions for different target groups. | ||||||||
| Bibliography | |||||||||
| No. | Reference | ||||||||
| Required Reading | |||||||||
| 1 | Brown, H. and Prescott, R. Applied Mixed Models in Medicine. 3rd edition, 2015. | ||||||||
| Additional Reading | |||||||||
| 1 | Verbeke, G. and Molenbergs, G. Linear mixed models for longitudinal. Springer Verlag, New York, 2008. | ||||||||
| 2 | Crawley, M. J. The R Book. 2nd edition. John Wiley&Sons, Ltd. 2013. | ||||||||
